A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan Page 171 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 ABSTRACT The current Japanese night-time emergency health care system can no longer meet patient demand due to the change in the patient’s consultation behavior and the doc- tor's uneven distribution. We propose a technique for mod- eling the whole processes of night-time emergency health care, the patient’s consultation behavior process, the pa- tient transportation process, and the negotiation process between a medical institute and an ambulance in order to simulate the current situation of night-time emergency health care in Japan. To construct this model, we use an agent-based modeling(ABM) approach that can take each decision making of the patient, the medical institution, and the fire department into consideration. This model allows us to evaluate improvement plans, especially focusing on the hospital rotation and the facility location. This study aims to build a simulation tool to achieve a sustainable medical treatment system, an efficient use of the limited medical resource and an improvement of medical service level. INTRODUCTION Recently, the emergency patient's acceptance refusal is becoming a problem at night-time in the city outskirts in Japan. The cause that the medical institution cannot re- spond to the patient transportation includes "Unprofessional", "Doctor is treating other patient", "All bed is fulfilled", and "Absence of doctor", and so on. The acceptance refusal by the medical institution can cause the delay of the transportation time of the patient, and that be- comes one of the important factors that control patient's prognosis. Therefore, an immediate improvement is neces- sary for the Japanese night-time emergency health care system. The causes of the increasing number of emergency patient's acceptance refusal are increasing demand of emer- gency patient caused by an aging society, and the doctor's uneven distribution caused by the clinical training system revision (Development Bank of Japan , 2009). The prob- lem of the doctor's uneven distribution is especially serious. Differences of the number of doctors between districts or hospital departments have extended, while the total of the doctor increases gradually. As a result, in a part of the dis- tricts or the hospital departments, the number of doctor is insufficient, and that is the main cause of the increase in the number of the emergency patient's acceptance refusal at night (Okamoto, 2010). For these reasons, a lot of cities and districts are work- ing on the improvement of the night-time emergency health care system. Especially, daily rotation systems that offer treatment for severe patient who need hospitalization by cooperating with hospitals in the same district on a rotation basis have already been introduced in a lot of districts. However, the running conditions of daily rotation systems are different in each district, and currently daily rotation systems do not function optimally. Therefore, the con- struction of a new night-time emergency health care system is necessary to enhance the daily rotation systems (Ministry A SIMULATION MODEL FOR ANALYZING THE NIGHT-TIME EMERGENCY HEALTH CARE SYSTEM IN JAPAN Yusho Kasuga Tokyo Institute of Technology ksuga09@cs.dis.titech.ac.jp Manabu Ichikawa Tokyo Institute of Technology ichikawa@dis.titech.ac.jp Hiroshi Deguchi Tokyo Institute of Technology deguchi@dis.titech.ac.jp Yasuhiro Kanatani National Institute of Public Health ykanatani@niph.go.jp mailto:ksuga09@cs.dis.titech.ac.jp mailto:ichikawa@dis.titech.ac.jp mailto:deguchi@dis.titech.ac.jp mailto:ykanatani@niph.go.jp Page 172 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 of Health, Labour and Welfare, 2008). Generally, the Japanese night-time emergency health care system is composed of the emergency transportation section by the fire department, and the emergency medical treatment section by the medical institution. For improve- ment of the night-time emergency health care, technical improvement of both sections and close cooperation be- tween them are important. Furthermore, it is necessary to consider the geographic population distribution, the facility location, and the differences in the patient’s consultation behavior, the transportation route, and the treatment proc- ess according to the patient type in order to discuss the de- lay of the patient transportation time. In this study, we construct a simulation model that reproduced the current night-time emergency health care system operated by the fire department and the medical institution. Moreover, the problem of the emergency pa- tient acceptance refusal is reproduced as a dynamic process by considering the decision making of the patient, the fire department, and the medical institute. As a result, using this simulation model, we can evaluate an applicable im- provement plan to the real situation. This study aims to build a simulation tool to achieve a sustainable medical treatment system, an efficient use of the limited medical resource and an improvement of medical service level. METHODOLOGY LITERATURE REVIEW We can divide previous works that take a simulation approach to the problem of emergency health care roughly into two categories. One is the research about the ambu- lance transportation section operated by the fire depart- ment, and another one is the research about the emergency medical treatment section operated by the medical institu- tion. Regarding the research about the ambulance transpor- tation section, there is the analysis by Oyama (Oyama, 2000). Oyama works on the optimum location problem of the firehouse to minimize accommodation time, the dura- tion from patient’s calling an ambulance to the accommo- dation by the medical institution, and show the optimal location of the firehouse considering the patient demand. However, because the entire problem is modeled by the top -down approach as the patient division is not considered, and patient's emergency demand is modeled by a Poisson distribution, and detailed emergency elements like the dif- ference in the transportation route are not considered. Moreover, this formulation is difficult to apply to a real problem, because the constraints of the medical institution that is involved in the patient accommodation, and the exis- tence of patients who visits the medical institution on their own way are not considered. Regarding the research about the emergency medical treatment section, there is an analysis by Matsumoto (Matsumoto, 2001). Matsumoto works on the optimization problem to minimize the patient’s travel distance for the improvement of efficiency of the daily rotation system dis- cussed in our study. As a result, it was shown that an im- provement in patient’s travel distance was expected by changing the rotation of the hospital. Discussion of the access cost of the night-time emergency health care in Ja- pan requires having into account the patient’s transporta- tion time. For this reason, this study is also difficult to ap- ply to a real problem. METHODOLOGY OF THIS RESEARCH In this study, we use an ABM approach to discuss the delay of the patient’s transportation time. An applicable model is constructed by modeling the difference between the patient’s consultation behavior and the patient’s trans- portation route according to the patient’s age, disease type, and disease level, at the same time, the consideration of characteristics of the population distribution and the facility location. Moreover, the patient accommodation condition of the medical institution is set as a constraint actually suited, and the current problem of the emergency patient acceptance refusal is reproduced by using a bottom-up ap- proach. Using these methodologies, we aim to propose an applicable improvement plan to a real problem as a result. MODEL OUTLINE OF THE MODEL The medical institution, the firehouse, human, and the ambulance in the night-time emergency health care are modeled. Human is classified into three cohorts according to the age, and the patient is generated according to the incidence probability of each cohort. The medical institu- tion has the diagnosis and treatment departments, and the patient consults the diagnosis and treatment department that is appropriate for one’s disease type. At this moment, pa- tients stochastically choose either of the two routes, con- sulting by oneself, by taxi or by private car, or using ambu- lance transportation according to the disease type, the dis- ease level, and the age. If using ambulance transportation, the ambulance, not patient, selects the medical institution according to the patient type. After accommodating patient by the medical institution, the triage is done in the medical institute, and from the serious case, diagnosis and treatment will be done. The ambulance is requested again when transferring the patient is necessary after diagnosis and treatment, and it is transferred to a suitable medical institu- tion. In this study, a simulation model that reproduced a series of process in the night-time emergency health care at the above-mentioned is constructed. The situation treated in this study is shown in Figure 1. Page 173 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 DEFINITION OF THE MODEL  Place The place is a demand point where human lives, and it is expressed as a node. A set of place is defined as follow: Place={Pi|i=1,2,…,p} A set of place has a coordinate values described as xPi and yPi. And a set of place has a population, a number of child described as cni, a number of adult described as ani, and a number of old man described as oni.  Medical institute Medical institute is defined as the place that has a role to accommodate patient and provide diagnosis and treat- ment, is expressed as a node. Medical institute’s function level is divided into three types according to their service level. Each function level is defined as follow: a. Primary-level emergency medical institute: That can treat emergency patient who doesn’t need inpatient hospital care. b. Secondary-level emergency medical institute: That can treat sever emergency patient who need inpatient hospital care. c. Thirdly-level emergency medical institute That can treat sever emergency patient who cannot be treated at the secondary-level medical in- stitute. Medical institute has diagnosis and treatment depart- ments. Correspondence with the actual diagnosis and treat- ment department is shown in Table 1 as a definition of the diagnosis and treatment department that treated in this study. A set of medical institute is defined as follow: Medical Institute={Mj | j=1,2,…,m} A set of medical institute has a coordinate values xPi and yPi, a function defined as FunctionType={FTi | pri- mary, secondary, thirdly}, a condition of internal medicine defined as InternalCondition={ICj | not acceptable, ac- ceptable}, a condition of surgery defined as SurgeryCon- dition={SCj | not acceptable, acceptable}, a condition of pediatric defined as PediatricCondition={PCj | not accept- able, acceptable}, a number of bed described as bnj, a de- partment list of day shift described as SettingDepart- mentListj, a department list of night shift described as a AcceptableDepartmetListj, and a waiting patient list of each The Situation Treated in this Study Figure 1 Page 174 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 hospital described as WaitingPatientListj.  Firehouse Firehouse is defined as the place that is a waiting place for the ambulance, is expressed as a node. A set of fire- houses is defined as follow: Firehouse={Fk | i=1,2,…,f} A set of firehouses has a coordinate values xPi and yPi, a list of place of incidence of patient is described as Inci- dencePlaceListk, a list of medical institute divided by the function and the department described as InternalPri- maryListk, SurgeryPrimaryListk, PediatricPrimaryListk, InternalSecondaryListk, SurgerySecondaryListk, Pedi- atricSecondaryListk, InternalThirdlyListk, SurgeryThirdly- Listk, PediatricThirdlyListk.  Human Human is classified into three cohorts according to the age. Each cohort is defined as follow: a. Child: from 0 to 15 years. b. Adult: from 16 to 64 years. c. Old: over 65 years. Human has a disease type and a disease level that de- termine the patient type after incidence as a patient. The definition of disease type divided based on the International Classification of Diseases (ICD) that is established by World Health Organization (WHO) is shown in Table 2. The definition of disease level and the necessary function level of medical institute corresponding with each disease level are shown in Table 3. Definition of diagnosis and treatment departments Table 1 Department in this study Actual department Neurology Neurology, Neurosurgery Cardiology Cardiology, Cardiovascular Surgery Gastroenterology Gastroenterology, Gastrointestinal Surgery Pulmonology Respiratory Medicine, Thoracic Surgery Psychiatry Psychiatry Urology Urology Internal Internal medicine Surgery Surgery, Orthopedic Surgery, emergency Pediatric Pediatrics Definition of disease type Table 2 Disease type ICD code Brain disease a-0904, a-0905 within Ⅸ Heart disease a-0901, a-0902, a-0903 within Ⅸ Digestive disease Ⅺ Respiratory disease Ⅹ Mental disease Ⅴ Sense organ disease Ⅵ, Ⅶ, Ⅷ Urologic disease XIV Internal disease Ⅰ, Ⅲ, Ⅳ, XⅡ, XV, XVI, XⅦ, XVⅢ, XX, XXI, XXⅡ External disease XⅢ, XⅨ Page 175 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 A set of human is defined as follow: Human={Hi|i=1,2,…,h} A set of human has an age class described as Age- Class={ACi | child, adult, old}, a disease type described as DiseaseType={DTs | s=1,2,…,10}={brain disease, heart disease, digestive disease, respiratory disease, mental dis- ease, sense organ disease, urologic disease , internal dis- ease, external disease}, a disease level described as Dis- easeLevel={DLt | t=1,2,3}={slight, moderate, advanced}, a consultation route that represent the way of patient consul- tation described as ConsultationRoute={CRu | u=1,2}= {using ambulance transportation, by oneself }. A list of candidate medical institute for consulting that is referred by the patient choosing consultation route of by oneself de- scribed as FeasinbleHospitalListi, a incidence probability according to the age class, the disease type, and the disease level described as P1{ACi∩DTs∩DLt}, a probability that patient choose the consultation route of using ambulance transportation described as P2{(ACi∩DTs∩DLt) ∩MMu|u=1}, and a probability that patient choose the con- sultation route of by oneself described as P3 {(ACi∩DTs∩DLt)∩MMu|u=2}=1-P2.  Ambulance A set of ambulance is defined as follow: Ambulance={Aj|j=1,2,…,n} A set of ambulance has an assigned firehouse de- scribed as AssignedFirehouse={AFk | k=1,2,…,n}, and a Definition of disease level and necessary function level of medical institute Table 3 Disease level Definition Necessary function level Slight Disease level that doesn’t need inpatient hospital care More than primary Moderate Disease level that needs inpatient hospital care within 3 weeks More than secondary Severe Disease level that needs inpatient hospital care over 3 weeks More than secondary Flow of the model Figure 2 Page 176 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 list of candidate medical institute that is referred by the ambulance transporting a patient chooses consultation route of using ambulance transportation described as Candidate- HospitalListj. FLOW OF THE MODEL Flow of the model is shown in Figure 2. In this section, there are some explanations about the details of each phase of the model. INCIDENCE OF PATIENT  Patient is generated according to the incidence prob- ability P1{ACi∩DTs∩DLt}. CHOOSE CONSULTATION ROUTE  Patient choose the consultation route according to the probability P2{(ACi∩DTs∩DLt)∩MMu|u=1} or P3 {(ACi∩DTs∩DLt)∩MMu|u=2} CHOOSE MEDICAL INSTITUTE TO CONSULT 1. According to their own patient type, a patient who choose the consultation route of by oneself makes a list of candidate medical institutes, FeasinbleHospitalListi, from InternalPrimaryListk, SurgeryPrimaryListk, Pedi- atricPrimaryListk, InternalSecondaryListk, Surgery- SecondaryListk, or PediatricSecondaryListk, 2. Within FeasinbleHospitalListi, patient chooses the nearest one to consult, and moves to that medical insti- tute.  Correlation between patient type and diagnosis and treatment department is shown in Table 4. CHOOSE CANDIDATE MEDICAL INSTITUTE  According to the patient type, ambulance which trans- ports the patient who choose the consultation route of using ambulance transportation makes a list of candi- date medical institute, CandidateHospitalListj, from InternalPrimaryListk, SurgeryPrimaryListk, Pedi- atricPrimaryListk, InternalSecondaryListk, Surgery- SecondaryListk, PediatricSecondaryListk, Inter- nalThirdlyListk, SurgeryThirdlyListk, PediatricThirdly- Listk. REQUEST FOR ACCEPTANCE  According to CandidateHospitalListj, an ambulance makes a request for accommodation of patient to the nearest one.  If the request is refused, an ambulance makes a request to the second nearest one.  If the request to the second nearest one is refused, an ambulance makes a request one after the other. CHOOSE ACCEPTANCE  According to the constraint of accommodation of pa- tient, a medical institute chooses to accept or refuse the request of accommodation of patient from an ambu- lance.  Constraint of accommodation of patient is defined as Correlation between patient type and diagnosis and treatment department Table 4 Disease type Age class Child Adult Old Brain disease Pediatric Internal Internal Heart disease Pediatric Internal Internal Digestive disease Pediatric Internal Internal Respiratory disease Pediatric Internal Internal Mental disease Pediatric Internal Internal Sense organ disease Pediatric Internal Internal Urologic disease Pediatric Internal Internal Internal disease Pediatric Internal Internal External disease Surgery Surgery Surgery Page 177 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 follow: [1] About a number of bed.  bnj > 0. [2] About a condition of each diagnosis and treat- ment department.  If the patient need a diagnosis and treatment by internal department,  ICj = acceptable.  If the patient need a diagnosis and treatment by surgery department,  SCj = acceptable.  If the patient need a diagnosis and treatment by pediatric department,  PCj = acceptable. ACCOMODATION OF PATIENT  Medical institute accommodate the patients who choose the consulting route of by oneself, and the patients who choose the consulting route of using ambulance, and meet constraint of accommoda- tion of patient. TRIAGE  Because the secondary and thirdly emergency medical institutes have the role of treating sever patient, triage is done in those medical institute, and from the serious case, diagnosis and treatment will be done. DIAGNOSIS AND TREATMENT  Treating time of each patient that is determined ac- cording to their disease level is defined as st for the slight, mt for the moderate, and at for the advanced.  During the treatment, a condition of each department of each medical institute, ICj, SCj, and PCj, is set to “not acceptable”. INPATIENT, TRANSFER, GO HOME  From the definition of disease level of patient, patient who have a moderate or advanced level of disease need to be inpatient, and the other one, slight level of disease will go back home after diagnosis and treat- ment.  When the patient need to be inpatient,  if the medical institute they consult has a special department correspond with their disease type,  the patient can be inpatient in that hos- pital.  if the medical institute they consult has a special department correspond with their disease type,  the patient needs to be transferred to other medical institute that has a specialized de- partment corresponding to one’s disease type.  Correlation between disease type and special depart- ment is shown in Table 5. EVALUATION INDEX In this study, we will evaluate the results of the simula- tion from the view of both the patient and the doctor who is the main stakeholder in the night-time emergency health Correlation between disease type and special department Table 5 Disease type Specialized department Brain disease Neurology Heart disease Cardiology Digestive disease Gastroenterology Respiratory disease Pulmonology Mental disease Psychiatry Sense organ disease Internal Urologic disease Urology Internal disease Internal External disease Surgery Page 178 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 care system. The patient transportation time, until being accommodated from the incidence of disease, and the doc- tor working hour are evaluated. Patient transportation time and the doctor working hour have a trade-off relation, if we optimize one side, the other side is deteriorated. Therefore, the improvement of the night-time emergency health care system is expected by deriving a preferable combination of both of these evalua- tion indexes. Then, in this study, we derive the preferable daily rotation system from the point of both the patient transportation time and the doctor working hour. An exam- ple of the rotation table that has been used in the daily rota- tion systems in Japan is shown in Table 6. Moreover, the number of waiting patient of each hos- pital is used as an evaluation index for analyzing the bal- ance between the distribution of medical resource and the distribution of patient demand. From the view of policy making, this type of analysis is necessary and indispensa- ble. Since we focus on the night-time, the road congestion situation is not considered in this study. Therefore, we as- sume that the ambulance moves at a fixed velocity at 60 km/h. Moreover, the speed of the taxi or private car, patient who choose consulting route of by their own uses, is as- sumed to move at a fixed velocity at 30 km/h that is the average speed in the residential area. And, we use Euclid- ean distance between the coordinates of each node as a traveling distance. An example of the result obtained by using this model is shown as follows. The patient transportation time is shown in Table 7, and the doctor working hour is shown in Table 8. Moreover, the number of waiting patient of each An example of expression of daily rotation system (m = 7) Table 6 Day Department M1 M2 M3 M4 M5 M6 M7 1 Internal ● Surgery ● Pediatric ● … … … Cardiology ● 2 Internal ● ● Surgery ● Pediatric … … … Cardiology … … … 30 Internal ● Surgery ● Pediatric ● … … … Cardiology ● Page 179 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 medical institute is shown in Figure 3. Using these results, we can evaluate the health care planning from both a micro and a macro viewpoint. From a micro level, it is repre- sented by the patient transportation time and the doctor working hour, and from a macro level, it is represented by the number of waiting patient of each medical institute. CONCLUSION AND FUTUREWORK In this paper, we propose a new technique of construct- ing simulation model that can take decision making of the patient, the fire department, and the medical institute into consideration by using an ABM approach. As a result, it is possible to derive an applicable improvement plan to a real situation focusing on the delay of patient transportation, which was not possible using traditional optimization ap- proach. Regarding the construction and the execution envi- ronment of the simulation model mentioned above, we use SOARS that is a simulation language designed for social simulation (Ichikawa, 2007). We choose SOARS because it doesn’t need an excellent program skill to modify the model. As future work, we will collect the real data to forecast detailed emergency patient demand. Moreover, we will collect data from not only the investigation concerning the realities of the patient consultation behavior and ambulance transportation but also from a geographic viewpoint in that case. As a result of these works, it is possible to extend this model to deal with location problems and routing problems of the fire department, the medical institute, and the ambu- lance. According to the opinion from some specialist, Patient transportation time [min] (patient who has an age class of child) Table 7 Age class Disease level Disease type Mean Child Slight Brain disease 29.56 Heart disease 37.78 Digestive disease 33.33 Respiratory disease 37.63 Mental disease 49.00 Sense organ disease 33.33 Urologic disease 29.00 Internal disease 29.88 External disease 44.00 Moderate Brain disease 27.99 Heart disease 39.10 Digestive disease 43.67 Respiratory disease 34.68 Mental disease 84.00 Sense organ disease 34.87 Urologic disease 31.14 Internal disease 27.07 External disease 29.75 Advanced Brain disease 28.87 Heart disease 39.00 Digestive disease 35.25 Respiratory disease 41.30 Mental disease 28.00 Sense organ disease 29.00 Urologic disease 33.00 Internal disease 58.00 External disease 33.33 Page 180 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 “Although it is ideal to have a uniform rotation system, that would increase the workload of some specific depart- ments”. From this point of view, it is important to concen- trate on obtaining desirable daily rotation system in the future. For this reason, it will be necessary to clarify the method of analysis and the target function. Especially, the cost analysis intended for the working cost of the doctor is important. Therefore, we must fix an evaluation index through the investigation concerning doctor's working envi- ronment in the future. In order to discuss the health care planning, it is neces- sary to analyze from a policy viewpoint. Therefore, we will conduct a monetary cost analysis under the assumption that we may need to hire additional doctors and/or may need to increase the budget. As an application of this model to other situation, we think it is useful as a simulation tool for a crisis manage- ment under the case of emergency situations, pandemic or disaster. In this model, we can change incidence probabil- ity outside of a model. And we have reproduced general emergency health care system in Japan, composed of the emergency transportation section and the emergency medi- cal treatment section. For those reasons, we can analyze a limitation of the current Japanese emergency health care system by changing incidence probability in the some spe- cific areas. Moreover, we can consider the desirable sys- tem design to increase a limitation of the current Japanese emergency health care system. Although we only focused on a case of night-time emergency health care as one exam- ple of some emergency situations in this study, we aim that Doctor working hour [hour] (doctor who works in M1) Table 8 Medical institute Department Hours M1 Neurology 168 Cardiology 392 Gastroenterology - Pulmonology - Psychiatry - Urology - Internal 392 Surgery 392 Pediatric - Number of waiting patients Figure 3 Page 181 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 this model will be applied to some other emergency cases, and used as a tool for a crisis management. As an application to other case, in the future, this model is useful as a simulation tool for a crisis manage- ment under the case of emergency situations such as pan- demic or disaster. In this model, we can change incidence probability in order to examine such emergency situations. Moreover, we have reproduced general emergency health care system in Japan that composed of the emergency transportation section and the emergency medical treatment section. For those reasons, we can analyze a limitation of the current Japanese emergency health care system by changing incidence probability in the some specific areas. Therefore, we can consider the desirable system design to increase a limitation of the current Japanese emergency health care system. Although we only focused on a case of night-time emergency health care as one example of some emergency situations in this study, we aim that this model will be applied to some other emergency cases, and used as a tool for a crisis management. 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Introductuon as a social microscope”, Proceedings of the 38th Annual Conference of the International Simula- tion and Gaming Association,P-36 http://www.dbj.jp/pdf/findicate/no130.pdf http://www.fasd.or.jp/tyousa/pdf/21-6hansoukyohi.pdf http://www.fasd.or.jp/tyousa/pdf/21-6hansoukyohi.pdf http://www.mhlw.go.jp/shingi/2008/07/dl/s0730-21a.pdf http://www.mhlw.go.jp/shingi/2008/07/dl/s0730-21a.pdf http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://ci.nii.ac.jp/els/110003481174.pdf?id=ART0004102737&type=pdf&lang=en&host=cinii&order_no=&ppv_type=0&lang_sw=&no=1289818087&cp http://toshisv.sk.tsukuba.ac.jp/Thesis/H16_2004/final/200101050.pdf http://toshisv.sk.tsukuba.ac.jp/Thesis/H16_2004/final/200101050.pdf Table of Contents Volume 38, 2011 Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise From Business Games to Simulations - Simuworlds & Microworlds Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations Managing Client-Based Learning: Insights from Successful Teaching Project Courses in Marketing Tracking Forecast Error Type, Frequency and Magnitude with the Forecast Error Package Responding to Facilitate Collaboration Simulating Sudden Change and the Value of Timely Information Managing Human Resources Simulation A Study on Collectivism and Group Decision-Making: An International Comparison of Japan, China, and Russia Using a Gaming Simulation The Use of Management Games in the Management Research Agenda Gaming On-Line: A Simulation Application Positioning and Performance in Simulated Networks Supply Chain Management: A Simulation Application Simulation as a Teaching Method in Strategic Management Distance Studies Entrepreneurship: A Game of Risk and Reward Phase II: The Start-Up Return to the Paradise Islands: From Confrontation to Cooperation Effect on Market Performance of Displaying Supply and Demand Curves in a Business Simulation Appreciating Complexity: The Chief of Staff of the Army Game Managing Organizations: Experiential MBA Course Teaches Alternatives to the Machine Model A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan The Continuing Evoluation of Assessing Project Management as an Academic Learning Outcome (ALO) Should College Instructors Change Their Teaching Styles to Meet the Millenial Student? The Mouse Game and its Effects on Team Interdependence Learning from the Gulf Oil Spill to Prepare for a Brighter Future: A New Game Engaging Stake Holders in Triple Bottom Line Accounting & Strategic Planning Video Killed the Biblio Star: The Impact of Digital Media on Student Learning Outcomes Exploring Motivation: Using Emoticons to Map Student Motivation in a Business Game Exercise An Alternative to PC and Internet Based Simulations: The Internet Integrated Mode MiddleState University -- A Crisis in Education Complexity Avoidance, Narcissism and Experiential Learning Examining the Cognitive, Affective, and Psychomotor Dimensions in Management Skill Development Through Experiential Learning: Developing a Framework A Situational Leadership Exercise Based on the Biology of a Starfish JOGAI CEFET -- The Industrial Administration Undergraduate Game Would You Take a Marketing Man to a Quick Service Restaurant? Modeling Corporate Social Responsibility In A Food Service Menu-Management Simulation Use of a Simulation in a Large Class Environment for a Marketing Principles Class: A Qualitative Analysis of Whether Learning Objectives were Met A Team Based Information Literacy Exercise ABSEL Marketing Communications Plan An Interdisciplinary Study of the Impact of Playing a Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles Analyzing Construction Planning of Interiro Finish Work of Apartment Building by Simulation Doing Murder One Again The Simple Business Game and Simulation Transfering the Knowledge of Middle Management to Novices Infectious Disease Simulation Model for Estimation of Spreading Understanding the Relative Influence of Several Factors in ERP Simulation Performance: An Exploration of Ecological Validity Tragedy of the Commons: An Exercise Using Clickers to Illustrate and Teach a Key Concept in Negotiations The Meaning of Firm Demand in Business Simulations If the Games Work, Why Aren't More Faculty Willing to Play? Those Who Do and Those That Don't: A Study of Engaged and Disengaged Business Game Players